The
Triple Helix of University-Industry-Government Relations is compared with
alternative models for explaining the current research system in its social
contexts. Communications and negotiations between institutional partners
generate an overlay that increasingly reorganizes the underlying
arrangements. The institutional layer can be considered as the retention
mechanism of a developing system. For example, the national organization
of the system of innovation has historically been important in determining competition.
Reorganizations across industrial sectors and nation states, however, are
induced by new technologies (biotechnology, ICT). The consequent
transformations can be analyzed in terms of (neo‑)evolutionary
mechanisms. University research may function increasingly as a locus in
the "laboratory" of such knowledge-intensive network transitions.

1.
Introduction: From the Endless Frontier to an Endless Transition

The
"Triple Helix" thesis states that the university can play an enhanced
role in innovation in increasingly knowledge‑based societies. The
underlying model is analytically different from the National Systems of
Innovation (NSI) approach (Lundvall 1988 and 1992;
Nelson 1993), which considers the firm as having the leading role in innovation,
and from the "Triangle" model of Sábato
(1975), in which the state is privileged (cf. Sábato
and Mackenzie 1982). We focus on the network overlay of communications
and expectations that reshape the institutional arrangements among
universities, industries, and governmental agencies.

As
the role of the military has decreased and academia has risen in the
institutional structures of contemporary societies, the network of
relationships among academia, industry, and government have also been transformed,
displacing the Cold‑War "Power Elite" trilateral mode of Wright
Mills (1958) with an overlay of reflexive communcations
that increasingly reshape the infrastructure (Etzkowitz
and Leydesdorff 1997). Not surprisingly, the effects
of these transformations are the subject of an international debate over the
appropriate role of the university in technology and knowledge transfer.
For example, the Swedish Research 2000 Report recommended the withdrawal
of the universities from the envisaged "third mission" of direct
contributions to industry (see Benner and Sandström,
this issue). Instead, the university should return to research and
teaching tasks, as traditionally conceptualized. However, it can be
expected that proponents of the third mission from the new
universities and regional colleges, which have based their research programmes on its premises, will continue to make their
case. Science and technology have become important to regional
developments (e.g., Braczyk et al.
1998). Both R&D and higher education can be analyzed also in terms of
markets (Dasgupta and David, 1994).

The
issues in the Swedish debate are echoed in the critique of academic technology
transfer in the U.S.A. by several economists (e.g., Rosenberg and
Nelson, 1994). The argument is that academic technology transfer
mechanisms may create unnecessary transaction costs by encapsulating
knowledge in patents that might otherwise flow freely to industry. But
would the knowledge be efficiently transferred to industry without the series
of mechanisms for identifying and enhancing the applicability of research
findings? How are development processes to be carried further, through
special grants for this purpose or in new firms formed on campus and in
university incubator facilities?

The
institutional innovations aim to promote closer relations between faculties and
firms. "The Endless Frontier" of basic research funded as an
end in itself, with only long-term practical results expected, is being
replaced by an "Endless Transition" model in which basic research is
linked to utilization through a series of intermediate processes (Callon 1998), often stimulated by
government.

The
linear model either expressed in terms of "market pull" or
"technology push" was insufficient to induce transfer of knowledge
and technology. Publication and patenting assume different systems of
reference both from each other and with reference to the transformation of
knowledge and technology into marketable products. The rules and
regulations had to be reshaped and an interface strategy invented in order to
integrate "market pull" and "technology push" through new
organizational mechanisms (e.g., OECD 1980; Rothwell
& Zegveld 1981).

In
the U.S.A., these programs include the Small Business Innovation Research
program (SBIR) and the Small Bussiness Technology
Transfer Program (STTR) of the Department of Defense, the Industry/University
Cooperative Research Centers (IUCRC) and Engineering Research Centers (ERC) of
the National Science Foundation, etc. (Etzkowitz
et al., 2000). In Sweden, the Knowledge
Competency Foundation, the Technology Bridge Foundation were
established as public venture capital source, utilizing the Wage Earners Fund,
originally intended to buy stock in established firms on behalf of the
public. The beginnings of a Swedish movement to involve
academia more closely in this direction has occasioned a debate similar
to the one that took place in the U.S. in the early
1980s. At that time, HarvardUniversity sought to establish a
firm jointly with one of its professors, based on his research results.

Can
academia encompass a third mission of economic
development in addition to research and teaching? How can each of these
various tasks contribute to the mission of the
university? The late nineteenth century witnessed an academic revolution
in which research was introduced into the university mission and made more or less
compatible with teaching, at least at the graduate level. Many universities in
the U.S.A. and worldwide are still undergoing this transformation of purpose. The
increased salience of knowledge and research to economic development has opened
up a third mission: the role of the university in economic
development. A "Second Academic Revolution" seems under way
since W.W. II, but more visibly since the end of the Cold War (Etzkowitz, forthcoming).

In the U.S.A. in the 1970s, in
various Western European countries during the 1980s, and in Sweden at present, this
transition has led to a reevaluation of the mission and role of the
university in society. Similar controversies have taken place in Latin America, Asia, and elsewhere in Europe. The "Triple
Helix" series of conferences (Amsterdam, 1996; Purchase, New York, 1998;
and Rio de Janeiro, 2000) have provided a venue for the discussion of
theoretical and empirical issues by academics and policy analysts (Leydesdorff
and Etzkowitz, 1996 and 1998). Different
possible resolutions of the relations among the institutional spheres of
university, industry, and government can help to generate alternative
strategies for economic growth and social transformation.

2.
Triple Helix Configurations

The
evolution of innovation systems, and the current conflict over
which path should be taken in university‑industry relations, are reflected in the
varying institutional arrangements of university‑industry-government
relations. First, one can distinguish a specific historical situation
which one may wish to label "Triple Helix I." In this
configuration the nation state encompasses academia and industry and directs
the relations between them (Figure 1). The strong version of this model
could be found in the former Soviet Union and in Eastern European
countries under "existing socialism." Weaker versions were
formulated in the policies of many Latin American countries and to some extent
in European countries such as Norway.

A
second policy model (Figure 2) consists of separate
institutional spheres with strong borders dividing them and highly
circumscribed relations among the spheres, exemplified in Sweden by the noted Research
2000 Report and in the U.S. in opposition to the
various reports of the Government‑University‑Industry Research
Roundtable (GUIRR) of the National Research Council (MacLane
1996; cf. GUIRR 1998). Finally, Triple Helix III is generating a
knowledge infrastructure in terms of overlapping institutional spheres, with
each taking the role of the other and with hybrid organizations emerging at the
interfaces (Figure 3).

Figure
3

The
Triple Helix Model of University-Industry-Government relations

Figure
4

The
overlay of communications and expectations at the network level guides the
reconstruction of institutional arrangements

The
differences between the latter two versions of the Triple Helix arrangements
currently generate normative interest. Triple Helix I is largely viewed
as a failed developmental model. With too little room for "bottom
up" initiatives, innovation was discouraged rather than encouraged. Triple
Helix II entails a laissez-faire policy, nowadays also advocated as
shock therapy to reduce the role of the state in Triple Helix I.

In
one form or another, most countries and regions are presently trying to attain
some form of Triple Helix III. The common objective is to realize an
innovative environment consisting of university spin‑off firms, tri‑lateral
initiatives for knowledge‑based economic development, and strategic
alliances among firms (large and small, operating in different areas, and with
different levels of technology), government laboratories, and academic research
groups. These arrangements are often encouraged, but not controlled, by
government, whether through new "rules of the game," direct or indirect
financial assistance, or through the Bayh‑Dole
Act in the U.S.A. or new actors such as
the above mentioned foundations to promote innovation in Sweden.

3. The Triple Helix of Innovation

The
Triple Helix as an analytical model adds to the description of the variety of institutional
arrangements and policy models an explanation of their dynamics. What are
the units of operation that interact when a system of innovation is
formed? How can such a system be specified?

In
our opinion, typifications in terms of "national
systems of innovation" (Lundvall 1988; Nelson
1993); "research systems in transition" (Cozzenset al., 1990; Ziman 1994), "Mode 2"
(Gibbons et al., 1994) or "the post modern research system"
(Rip and Van derMeulen
1996) are indicative of flux, reorganization, and the enhanced role of
knowledge in the economy and society. In order to explain these
observable reorganizations in university-industry-government relations, one
needs to transform the sociological theories of institutional retention, recombinatorial innovation, and reflexive controls.
Each theory can be expected to appreciate a different subdynamic
(Leydesdorff 1997).

In
contrast to a double helix (or a coevolution between
two dynamics), a Triple Helix is not expected to be stable. The
biological metaphor cannot work because of the difference between cultural and
biological evolutions. Biological evolution theory assumes variation as a
driver and selection to be naturally given. Cultural evolution, however,
is driven by individuals and groups who make conscious decisions as well as the
appearance of unintended consequences. A Triple Helix in which each
strand may relate to the other two can be expected to develop an emerging
overlay of communications, networks, and organizations among the helices
(Figure 4).

The
sources of innovation in a Triple Helix configuration are no longer
synchronized a priori. They do not fit together in a pregiven order, but they generate puzzles for participants,
analysts, and policy-makers to
solve. This network of relations generates a reflexive subdynamics of intentions, strategies, and projects
that adds surplus value by reorganizing and harmonizing continuously the
underlying infrastructure in order to achieve at least an approximation of the
goals. The issue of how much we are in control or non-control of these
dynamics specifies a research program on innovation.

Innovation
systems, and the relationships among them, are apparent at the organizational,
local, regional, national, and multi‑national levels. The interacting subdynamics, that is, specific operations like markets and
technological innovations, are continuously reconstructed like commerce on the
Internet, yet differently at different levels. The subdynamics
and the levels are also reflexively reconstructed through discussions and
negotiation in the Triple Helix. What is considered as
"industry", what as "market" cannot be taken for granted
and should not be reified. Each "system" is defined and can be
redefined as the research project is designed.

For
example, "national systems of innovation" can be more or less
systemic. The extent of systemness remains an
empirical question (Leydesdorff and Oomes 1999). The dynamic "system(s) of
innovation" may consist of increasingly complex collaborations across
national borders and among researchers and users of research from various
institutional spheres (Godin and Gingras,
this issue). There may be different dynamics among regions. The
systems of reference have to be specified analytically, that is, as
hypotheses. The Triple Helix hypothesis is that systems can be expected
to remain in transition. The observations provide an opportunity to
update the analytical expectations.

4.
An Endless Transition

The
infrastructure of knowledge‑intensive economies implies an Endless
Transition. Marx's great vision that "all that is solid, melts into
air" (Berman 1982) underestimated the importance of seemingly volatile communications
and interactions in recoding the (complex) network system. Particularly,
when knowledge is increasingly utilized as a resource for the production and
distribution system, reconstruction may come to prevail as a mode of
"creative destruction" (Schumpeter 1939 and 1966; Luhmann
1984).

Can
the reconstructing forces be specified? One mode of specification is
provided by evolutionary economics in which the three functional mechanisms
are: technological innovation provides the variation, markets are the
prevailing selectors, and the institutional structures provide the system with
retention and reflexive control (Nelson 1994). In advanced and pluriform societies, the mechanisms of institutional
control are again differentiated into public and private domains. Thus,
a complex system is developed that is continuously integrated and
differentiated, both locally and globally.

Innovation
can be defined at different levels and from different perspectives within this
complex dynamics. For example, evolutionary economists have argued that one
should consider firms as the units of analysis, since they carry the
innovations and they have to compete in markets (Nelson and Winter 1982; cf.
Andersen 1994). From a policy perspective, one may wish to define
"national systems of innovation" as a relevant frame of reference for
government interventions. Others have argued in favour
of networks as more abstract units of analysis: the semi‑autonomous
dynamics of the networks may exhibit lock‑ins, segmentation, etc. (e.g.,
David and Foray 1994). Furthermore, the evolving networks may change in
terms of relevant boundaries while developing (Maturana 1978).

In
our opinion, these various perspectives open windows of appreciation on the
dynamic and complex processes of innovation, but from specific angles.
The complex dynamics is composed of subdynamics like
market forces, political power, institutional control, social movements,
technological trajectories and regimes. The operations can be expected to
be nested and interacting. Integration, for example, within a corporation
or within a nation state, cannot be taken for granted. Technological
innovation may also require the reshaping of an organization or a community
(Freeman and Perez 1988). But the system is not deterministic: in some
phases intentional actions may be more succesful in
shaping the direction of technological change than in others (Hughes 1983).

The
dynamics are non‑linear while both the interaction terms and the
recursive terms have to be declared. First, there are ongoing
transformations within each of the helices. These reconstructions can be
considered as a level of continuous innovations under pressure of changing
environments. When two helices are increasingly shaping each other mutually,
co‑evolution may lead to a stabilization along a
trajectory. If more than a single interface is stabilized, the formation
of a globalized regime can be expected. At each
level, cycles are generated which guide the phasing of the developments.
The higher‑order transformations (longer‑term) are induced by the
lower‑order ones, but the latter can seriously be disturbed by events at
a next‑order system's level (Schumpeter 1939; Kampmannet al. 1994).

Although
this model is abstract, it enables us to specify the various windows of
theoretical appreciation in terms of their constitutive subdynamics
(e.g., Leydesdorff & Van den Besselaar
1997). The different subdynamics can be
expected to select upon each other asymmetrically, as in processes of negotiation,
by using their specific codes. For example, the markets and networks
select upon technological feasibilities, whereas the options for technological
developments can also be specified in terms of market forces. Governments
can intervene by helping create a new market or otherwise changing the rules of
the game.

When the selections "lock‑in" upon each
other, next‑order systems may become relevant. For example,
airplane development at the level of firms generates trajectories at the level
of the industry in coevolutions between selected
technologies and markets (e.g., Nelson 1994, cf. McKelvey
1996). Nowadays, the development of a new technological trajectory
invokes the support of national governments and even international levels (like
the EU), using increasingly a Triple Helix regime (Frenken
and Leydesdorff, forthcoming).

We
have organized this theme issue about the Triple Helix of University‑Industry‑Government
Relations in terms of three such interlocking dynamics: institutional
transformations, evolutionary mechanisms, and the new position of the
university. This approach allows us to pursue the analysis at the network
level and then to compare among units of analysis. For example, both
industries and governments are entrained in institutional transformations,
while the institutional transformations themselves change under the pressure of
information and communication technologies (ICT) or government policies.
Before explaining the organization of the theme issue in detail, however, we
wish to turn briefly to the analytical position of the Triple Helix model in
relation to other non‑linear models of innovation, like "Mode
2" and "national systems of innovation."

5. Non‑linear models of innovation

As
noted, non‑linear models of innovation extend upon linear models by
taking interactive and recursive terms into account. These non‑linear
terms can be expected to change the causal relations between input and
output. The production rules in the systems under study, for example, can
be expected to change with the further development of the input/output
relations (e.g., because of economies of scale). Thus, the unit of
operation may be transformed, as is typical when a pilot plant in the chemical
industry is scaled up to a production facility.

By
changing the unit of analysis or the unit of operation at the reflexive level,
one obtains a different perspective on the system under study. But the
system itself is also evolving. In terms of methodologies, this challenges
our conceptual apparatus, since one has to be able to distinguish whether the
variable has changed or merely the value of the variable. The analysis
contains a snapshot, while the reality provides a moving picture. One needs
metaphors to reduce the complexity for the discursive understanding.
Geometrical metaphors can be stabilized by higher‑order codifications as
in the case of paradigms. The understanding in terms of fluxes (that is,
how the variables as well as the value may change over time), however, calls
for the use of algorithmic simulations. The observables can then be
considered as special cases which inform the expectations (Leydesdorff
1995).

Innovation, in particular, can be defined
only in terms of an operation. Both the innovator(s) and the innovated
system(s) are expected to be changed by the innovation. Furthermore, one
is able to be both a participant and an observer, and one is also able to
change perspectives. In the analysis, however, the various roles are
distinguished although they can sometimes be fused in "real life"
events. Langton (1989) proposed to distinguish
between the "phenotypical" level of the
observables and the "genotypical" level of
analytical theorizing. The "phenotypes" remain to be explained
and the various explanations compete in terms of their clarity and usefulness
for updating the expectations. Confusion, however, is difficult to avoid
given the pressure to jump to normative conclusions, while different
perspectives are continuously competing, both normatively and analytically.

Let
us first focus on the problem of the unit of
analysis and the unit of operation. In addition to extending the linear
(input/output) models of neo‑classical and business economics,
evolutionary economists also changed the unit
of analysis. Whereas neo‑classical economics focused on markets as
networks in terms of input/output relations among individual (rational) agents,
evolutionary economists have tended to focus on
firms as the specific (and bounded) carriers of an innovation process.
Both the unit of analysis and the unit of operation were changed (Andersen
1994; cf. Alchian 1950).

Lundvall (1988, at p. 357) noted that the interactive
terms between demand and supply in user‑producer relations assume a
system of reference in addition to the market. The classical dispute in
innovation theory had, in his opinion, referred to the role of demand and
supply, that is, market forces, in determining the rate and direction of the
process of innovation (cf. Mowery and Rosenberg, 1979; Freeman, 1982, p.
211). If, however, the dynamics of innovation (e.g., product competition)
are expected to be different from the dynamics of the market (e.g., price
competition), an alternative system of reference for the selection should also
be specified. For this purpose, Lundvall
proposed "to take the national system of production as a starting point
when defining a system of innovation" (p. 362).

Lundvall added that the national system of production
should not be considered as a closed system: "the specific degree and form
of openness determines the dynamics of each national system of
production." In our opinion, as a first step, innovation systems
should be considered as the dynamics of change in systems of both
production and distribution. From this perspective, national systems
compete in terms of the adaptability of their knowledge infrastructure.
How are competences distributed for solving "the production puzzle"
which is generated by uneven technological developments across sectors (Nelson
& Winter 1975; Nelson 1982)? The infrastructure conditions the
processes of innovation which are possible within and among the sectors.
In particular, the distribution of relevant actors contains an
heuristic potential which can be made reflexive by a strategic analysis of
specific strengths and weaknesses (Pavitt
1984).

The
solution of the production puzzle typically brings government into the picture shifting the
dynamics from a double to a triple helix. The consequent processes of
negotiation are both complex and dynamic: one expects that the (institutional)
actors will be reproduced and changed by the interactions. Trilateral
networks and hybrid organzations are created for
resolving social and economic crises. The actors from the different
spheres negotiate and define new projects, such as the invention of the venture
capital firm in New England in the early post-war era (Etzkowitz,
forthcoming). Thus, a Triple Helix dynamics of University‑Industry‑Government
Relations is generated endogeneously.

Gibbons
et al. (1994) argued that this "new mode of the production of
scientific knowledge" has become manifest. But: how are these
dynamics in the network arrangements between industries, governments, and
academia a consequence of the user‑producer interactions foregrounded by Lundvall
(1988)? Are national systems still a relevant unit of analysis?
Since the new mode of knowledge production ("Mode 2") is
characterized as an outcome, it should, in our opinion, be considered as an
emerging system. The emerging system rests like a hyper‑network
on the networks on which it builds (such as the disciplines, the industries,
and the national governments), but the knowledge‑economy transforms
"the ship while a storm is raging on the open sea" (Neurathet al., 1929).

Science
has always been organized through networks, and to pursue practical as well as
theoretical interests. Centuries before “Mersenne”, was transmogrified into an Internet site, he was
an individual, who by visits and letters, knitted the European scientific
community together. The Academies of Science played a similar role in local and
national contexts from the 16th century.

The
practical impetus to scientific discovery is
long-standing. Robert K. Merton's (1938)
dissertation reported that between 40-60% of discoveries in the 17th century
could be classified as having their origins in trying to solve problems in navigation,
mining, etc. Conversely, solution of practical problems through scientific
means has been an important factor in scientific development, whether in German
pharmaceutical science in the 17th century (Gustin
1975) or in the British sponsored competition to provide a secure basis for
navigation (Sobel, 1995).

The
so-called "Mode 2" is not new; it is the original format of science
before its academic institutionalization in the nineteenth century. Another
question to be answered is why "Mode 1" has arisen after "Mode
2": the original organizational and institutional basis of science,
consisting of networks and invisible colleges (cf. Weingart,
1997; Godin, 1998).Where have these ideas, of the
scientist as the isolated individual and of science separated from the interests of society, come
from? "Mode 2" represents the material base of science, how it
actually operates. "Mode 1" is a construct, built upon that base in
order to justify autonomy for science, especially in an earlier era when it was
still a fragile institution and needed all the help it could get.

In
the U.S.A., during the late 19th
century, large fortunes were given to
found new universities, and expand old ones. There were grave concerns
among many academics that the industrialists making these gifts
would try to directly influence the universities, by claiming rights to hire and fire professors as well as well
as to decide what topics were acceptable for research and instruction (Storr, 1953). To carve out an independent space for
science, beyond the control of economic interests, a physicist, Henry
Rowland, propounded the doctrine that if anyone with external interests tried to intervene, it
would harm the conduct of science. As President of the American Association for
the Advancement of Science, he promoted the ideology of pure research in the
late 19th century. Of course, at the same time as liberal arts
universities oriented toward pure research were
being founded, land grant universities, including MIT, pursued more
practical research strategies. These two contrasting academic modes existed in
parallel for many years.

Decades
hence, Robert K. Merton posited the normative structure of science
in 1942 and strengthened the ideology of “pure science.” His
emphasis on universalism and skepticism was a
response to a particular historical situation, the need to defend science from
corruption by the Nazi doctrine of a racial basis for science and from Lysenko’s attack on genetics in the Soviet Union. Merton’s formulation
of a set of norms to protect the free space of science was accepted as the
basis for an empirical sociology of science for many years.

The
third element in establishing the ideology of pure science was, of course, the
Bush Report of 1945. The huge success of science in supplying practical results
during World War II in one sense supplied its own legitimation
for science. But with the end of the war at hand and wanting to insure that
science was funded in peacetime, a rationale was needed in 1944 when Bush
persuaded President Roosevelt to write a letter commissioning the report (Bush
1980).

In
the first draft of his report, Bush proposed to
follow the then current British method of funding science at universities. It
would be distributed on a per capita basis according to the number of students
at each school. In the contemporary British system of a small number of
universities, the funds automatically went to an
elite. However, if that model had been followed in the U.S., even in the early post
war era, the flow of funds would have taken a different course. The funding
would not only have flowed primarily to a bi-coastal
academic elite but would have been much more broadly distributed across the
academic spectrum, especially to the large state universities in the Midwest.

In
the time between the draft and the final report, the mechanism for distribution
of government funds to academic research was revised and “peer review” was
introduced. Adapted from Foundation practices in the 1920s and 30s, it could be
expected that "the peers," the leading scientists who would most surely
be on those committees, would distribute the funds primarily to a scientific
elite. The status system of U.S. universities that had
been in place from the 1920s was reinforced.

This
model of “best science” is no longer acceptable to many as the sole basis for
distribution of public research funds. Congresspersons who represent regions
with universities that are not significant recipients of research funds have
disregarded peer review and distributed research funds by direct appropriation,
much as roads and bridges are often sited through “log rolling” and “pork
barrel” processes. Nevertheless, these politically directed funds support
also serious scientific research and instrumentation projects. Even when
received by schools with little or no previous research experience, these “one
time funds” are typically used to rapidly build up competencies in order to
compete within the peer review system.

Indeed,
when a leading school, ColumbiaUniversity, needed to renew the infrastructure
of its chemistry department, it contracted with the same lobbying firm in WashingtonDC as less well-known
schools. Through public relations advice, Columbia relabeled its chemistry
department "The National Center for Excellence in Chemistry." A
special federal appropriation was made and the research facilities were
renovated and expanded. To hold its faculty, the university could not afford to
wait for the slower route of peer review, and likely smaller amounts of
funding.

Increasing
competition for research funds among new and old actors has caused an incipient
breakdown of “peer review,” a system that could
best adjudicate within a moderate level of competition. As competition for
research funds continues to expand, how should the strain be
adjusted? Some propose shrinking the research system; others suggest linking
science to new sources of legitimation such as
regional development.

6.
The Future Legitimation of Science

It
is nowadays apparent that the development of science provides much of the basis
for future industrial development. These connections, however, have been
present from the creation of science as an organized activity in the 17th
century. Marx pointed them out again in the mid-19th century in connection with
the development of chemical industry in Germany. At the time, he
developed a thesis of the growth of science-based industry on the basis of a
single empirical example: Perkins researches on dyestuffs in the UK leading to the
development of an industry in Germany.

The
potential of science to contribute to economic development has become a source
of regional and international competition at the turn of the millenium. Until recently, the location of research was of
little concern. The relationship between the site where knowledge is produced
and its eventual utilization was not seen to be tightly linked, even as a first
mover advantage. This view has changed dramatically in recent years, as has the
notion that high-tech conurbations, like Route 128 and Silicon Valley, are unique instances
that can not be replicated. The more recent emergence of Austin, Texas, for example, is based
in part on the expansion of research at the University of Texas, aided by state as well
as industry and federal funds.

Less
research intensive regions are by now well aware that science, applied to local
resources, is the basis of much of their future potential for economic and
social development. In the U.S.A., it is no longer
acceptable for research funds to primarily go to the east and west coasts with a few places in
between in the Midwest. The reason why funding is awarded on bases
other than the peer review system, is that all regions want a share of research
funding

The
classic legitimation for scientific research as a
contribution to culture still holds and military and health objectives also
remain a strong stimulus to research funding. Nevertheless, the future legitimation for scientific research, which will keep funding
at a high level, is that it is increasingly the source of new lines of economic
development.

Newly
created disciplines are often the basis for these heightened expectations. Such
disciplines do not arise only from the subdivision of new disciplines from old
ones, as in the 19th century (Ben David and Collins, 1966). New
disciplines have arisen, more recently, through syntheses of practical and
theoretical interests. For example, computer
science grew out of elements of older disciplines such as electrical
engineering, psychology, philosophy, and a machine. Materials science and other
fields such as nano-technology that are on every
nation’s critical technology list were similarly created.

The
university can be expected to remain the core institution of the knowledge
sector as long as it retains its original educational mission (Etzkowitz,
Webster, Gebhardt, and Terra, this issue). Teaching
is the university’s comparative advantage, especially when linked to research
and economic development. Students are also potential inventors. They
represent a dynamic flow-through of “human capital” in academic research
groups, as opposed to more static industrial laboratories and research
institutes. Although they are sometimes considered a necessary distraction, the
turnover of students insures the primacy of the university as a source of
innovation.

The
university may be compared to other recently proposed contenders for knowledge
leadership, such as the consulting firm. A consulting company draws together
widely dispersed personnel for individual projects and then disperses them
again after a project, solving a client’s particular problem, is completed. Such
firms lack the organizational ability to pursue a cumulative research program
as a matter of course. The university’s unique comparative advantages is that it combines continuity with change,
organizational and research memory with new persons and new ideas, through the
passage of student generations. When there is a break in the generations,
typically caused by a loss of research funding, one academic research group
disappears and can be replaced by another.

Of
course, as firms organize increasingly higher level training programs (e.g., AppliedGlobalUniversity at the Applied
Materials Devices Corporation, a semi-conductor equipment manufacturer in Silicon Valley) they might in the
future also, individually or jointly, attempt to give out degrees. Companies
often draw upon personnel in their research units, as well as external
consultants, to do some of the teaching in their corporate universities.
Nevertheless, with a few notable exceptions, such as the RAND Corporation, they
have not yet systematically drawn together research and training into a single
framework. However, as the need for life-long learning increases, a university
tied to the workplace becomes more salient.

7.
Implications of the Triple Helix Model

The
Triple Helix denotes not only the relationship of university, industry and
government, but also internal transformation within each of these spheres. The
university has been transformed from a teaching institution into one which
combines teaching with research, a revolution that is still ongoing, not only
in the U.S.A., but in many other countries. There is a tension between the two
activities but nevertheless they co-exist in a more or less compatible
relationship with each other because it has been found to be both more
productive and cost effective to combine the two functions.

The
Triple Helix overlay provides a model at the level of social structure for the
explanation of "Mode 2" as an historically
emerging structure for the production of scientific knowledge, and its relation
to "Mode 1." First, the arrangements between industry and
government no longer need to be conceptualized as exclusively between national
governments and specific industrial sectors. Strategic alliances cut
across traditional sector divides; governments can act at national, regional,
or increasingly also at international levels. Corporations adopt
"global" postures either within a formal corporate structure or by
alliance. Trade blocks like the EU, NAFTA, and Mercosul
provide new options for breaking "lock‑ins," without the
sacrifice of competitive advantages from previous constellations. For
example, the Airbus can be considered as an interactive opportunity for
recombination at the supra‑national level (Frenken,
this issue).

Second,
the driving force of the interactions can be specified as the expectation of
profits. "Profit" may mean different things to the various
actors involved. A leading edge consumer, for example, provides firms and
engineers with opportunities to perceive "reverse salients"
in current product lines and software. Thus, opportunities for
improvements and puzzle‑solving trajectories can be defined. Note
that analytically the drivers are no longer conceptualized as ex ante causes,
but in terms of expectations that can be evaluated only ex post.
From the evolutionary perspective, selection (ex post) is structure
determined, while variation may be random (Arthur 1988; Leydesdorff
and Van den Besselaar 1998).

Third,
the foundation of the model in terms of expectations leaves room for
uncertainties and chance processes. The institutional carriers are
expected to be reproduced as far as they have been functional hitherto, but the
negotiations can be expected to lead to experiments which may thereafter also
be institutionalized. Thus, a stage model of innovation can be specified.

The
stages of this model do not need to correspond with product life cycle
theory. Barras (1990), for example, noted that in ICT "a reverse
product life" cycle seems to be dominant. Bruckneret al. (1994) proposed niche‑creation as the mechanism of potential
lock‑out in the case of competing technologies. A successful
innovation changes the landscape, that is, the opportunity structure for the
institutional actors involved. Structural changes in turn are expected to
change the dynamics.

Fourth,
the expansion of the higher‑education and academic research sector has
provided society with a realm in which different representations can be
entertained and recombined in a systematic manner. Kaghan
and Barett (1997) have used in this context the term
"desktop innovation" as different from the laboratory model (cf. Etzkowitz, 1999). Knowledge‑intensive economies
can no longer be based on simple measures of profit maximization: utility
functions have to be matched with opportunity structures. Over time,
opportunity structures are recursively driven by the contingencies of
prevailing and possible technologies. A laboratory of knowledge‑intensive
developments is socially available and can be improved upon (Etzkowitz and Leydesdorff
1995). As this helix operates, the human capital factor is further
developed along the learning curves and as an antidote to the risk of
technological unemployment (Pasinetti, 1981).

Fifth,
the model also explains why the tensions need not to be resolved. A
resolution would hinder the dynamics of a system which lives from the
perturbations and interactions among its subsystems. Thus, the subsystems
are expected to be reproduced. When one opens the black‑box one
finds "Mode 1" within "Mode 2," and "Mode 2"
within "Mode 1." The system is neither integrated nor
completely differentiated, but it performs on the edges of fractional
differentiations and local integrations. Using this model, one can begin
to understand why the global regime exhibits itself in progressive instances,
while the local instances inform us about global developments in terms of the
exceptions which are replicated and built upon.

Case
materials enable us to specify the negative selection mechanisms
reflexively. Selection mechanisms, however, remain constructs. Over
time, the inference can be corroborated. At this
end, the function of reflexive inferencing based on
available and new theories moves the system forward by drawing attention to
possibilities for change.

Sixth,
the crucial question of the exchange media —economic expectations (in terms of
profit and growth), theoretical expectations, assessment of what can be
realized given institutional and geographic constraints— have to be related and
converted into one another. The helices communicate recursively over time
in terms of each one's own code. Reflexively, they can also take the role
of each other, to a certain extent. While the discourses are able to
interact at the interfaces, the frequency of the external interaction is (at
least initially) lower than the frequency within each helix. Over time
and with the availability of ICT, this relation is changing.

The
balance between spatial and virtual relations is contingent upon the
availability of the exchange media and their codifications. Codified
media provide the system with opportunities to change the meaning of a
communication (given another context) while maintaining its substance (Cowan
and Foray 1997). Despite the "virtuality"
of the overlay, this system is not "on the fly": it is grounded in a
culture which it has to reproduce (Giddens
1984). The retention mechanism is no longer given, but "on the
move": it is reconstructed as the system is reconstructed, that is, as one
of its subdynamics.

As
the technological culture provides options for recombination, the boundaries of communities can be
reconstituted. The price may be felt as a loss of traditional identities
or alienation, or as a concern with the sustainability
of the reconstruction, but the reverse of "creative destruction" is
the option of increasing development. The new mode of knowledge
production generates an Endless Transition that continuously redefines the
borders of the Endless Frontier.

8. The organization of the theme issue

As
noted above, this issue is organized in three main parts, addressing (1)
institutional transformation, (2) evolutionary mechanisms, and (3) the second
academic revolution. Each part contains five contributions.

In
Part One ("Institutional Transformations"), Michael Nowak and Charles
Grantham open the discussion with a paper about the impact of the Internet on
incubation as an institutional mechanism for technological innovation.
The increased complexity of the process induces reflexivity about the choices
to be made, and human capital becomes increasingly crucial for carrying the
transformations.

The
failure of the "opening to the market" as an answer to the state‑dominated
economies in the former Soviet Union, because of the neglect of the knowledge‑intensive
dimension, is discussed by testing three models against each other in Judith Sedaitis' paper entitled "Technology Transfer in
Transitional Economies: Comparing Market, State, and Organizational
Frameworks." The author concludes that processes of transfer in
these cases can be understood at the intermediate network level.

Norma
Morris, in "Vial Bodies: Conflicting Interests in the Move to New
Institutional Relationships in Biological Medicines Research and
Regulation," discusses normative issues that arise when the borders are no
longer defined institutionally and governmentally. The case of the EU
places the role of safety regulation at national and transnational levels on
the agenda. In a paper entitled "The Evolution of Rules for Access
to Megascience Research Environments Viewed from
Canadian Experience," Cooper Langford and Martha Whitney Langford document
what it means for the organization of Canadian science that government and
industry relations are deeply involved in this enterprise. Are the Kudos‑norms
of Merton (1942) increasingly being replaced by a new set of norms (Ziman 1994)? If so, what are the expected effects on
reward systems and funding? In a contribution to the latter question,
Shin‑Ichi Kobayashi argues that a third form of
funding can be distinguished nowadays (in addition to peer recognition and
institutional allocation). The author develops the new format using the
metaphor of the audition system for the performing arts.

Thus,
not only the institutions themselves are tranformed,
but also their mechanisms of transformation. These evolutionary
mechanisms are central to the second part of the theme issue. The contribution from the Aveiro team
(Eduardo Anselmo de Castro, Carlos José Rodrigues, Carlos Esteves, and Arturda Rosa Pires)
returns to the impact of ICT on changing the stage. How can
institutional arrangements be shaped to match the options which telematics provide? How can a retention mechanism be
organized as a niche or a habitat for knowledge‑intensive developments?

While
the Portuguese team focuses on the regional level, Susanne Giesecke
takes the analysis to the level of comparing national governments in her
contribution entitled "The Contrasting Roles of Government in the Development
of the Biotechnology Industries in the U.S. and Germany." She notes
the counter‑effective policies of German governments which have operated
on the basis of assumptions about previous developments. Policies have to
be updated in terms of bottom‑up processes and thus come to be understood
in terms of reflexive feedbacks (instead of control).

RosalbaCasas, Rebeca de Gortari, and Ma.Josefa Santos from Mexicocombine
the issues of regional developments and differences between the technologies
involved by cross-tabling them for the case of Mexico. These authors
focus on what they call "the building of knowledge spaces." How
is the interrelationship between knowledge‑intensity,
industrial activity, and institutional control shaped in terms of inter‑human
and inter‑institutional relations? What is the function of shared
culture, values, and trust? Is the region a habitat for the technology,
or the technology a precondition for restructuring the region?

In
a contribution entitled "The Triple Helix: An Evolutionary Model of
Innovations," LoetLeydesdorff
uses simulations to show how a "lock‑in" can be enhanced using
a co‑evolution like the one between regions and technologies. A
third source of random variation, however, may intervene, reversing the order
in a later stage and leading to more complex arrangements of market
segmentation (that is, different suboptima). A
mechanism for "lock‑out" can also be specified.

KoenFrenken takes the complexity
approach one step further by confronting it with empirical data in the case of
the aircraft industry. Using Kauffman's (1993) model of "rugged
fitness landscapes" he shows the working of a Triple Helix in different
phases of this industry (cf. Frenken and Leydesdorff, forthcoming). The model can be extended
to account for the additional degree of freedom in international collaborations
to develop new aircraft. The failure of Fokker Aircraft, for example, can
be explained using these concepts: one cannot bet on two horses at the same
time, since the markets are fiercely competitive, technological infrastructures
are expensive, and learning curves are steep.

In
the third part of the issue, we turn to the Second Academic Revolution.
In their contribution entitled "The Place of Universities in the System of
Knowledge Production," BenoîtGodin and Yves Gingras argue
against the thesis that the university would have lost its salient position in
the university‑industry‑government relations of "Mode
2." Using scientometric data, they show
that collaboration with academic teams is central to the operations of the
networks which transform this knowledge infrastructure. Although based on
Canadian data, the argument is made that this holds true also for other OECD
countries.

From
another world region, Judith Sutz reports about
university‑industry‑government relations in Latin America. These young
democracies, on the one hand, wish to free themselves from the limitation of
the so‑called "import substitution" regime by opening up to the
market. On the other hand, the connections are then established through
the world system, and regional infrastructures tend to remain
underdeveloped. The issue will be central to the Third Triple Helix
Conference to be held in Rio de Janeiro, 26‑29 April
2000. How can social, economic, and scientific developments be networked
at the regional level? What does niche management mean in an open
system's environment?

In
a contribution entitled "Institutionalizing the Triple Helix: Research
Funding and Norms in the Academic System," Mats Benner and Ulf Sandström take a neo‑institutional approach to the
transformation of the university system in Europe. How does the
system react (resist and embody) institutional transformation and neo‑evolutionary
pressures? In a further article, Eric Campbell and his colleagues raise
the question of how this affects research practices in terms of "Data
Withholding in Academic Medicine." Can characteristics of faculty
denied access to research results and biomaterials be distinguished?

In
a final article, Henry Etzkowitz, Andrew Webster, ChristianeGebhardt, and Branca Terra substantiate their claim that the
transformation of the university system is a worldwide phenomenon. In
addition to research and higher eduction, the
university nowadays has a third role in regional and economic development
because of the changing nature of both knowledge production and economic
production. While a "hands off" may have been functional to
previous configurations, the exigencies of today demand a more intensive
interrelationship. As noted, a Triple Helix arrangement that tends to
reorganize the knowledge infrastructure in terms of possible overlays,
can be expected to be generated endogenously.

Acknowledgements

We
acknowledge support from the U.S. National Science Foundation, the European
Commission DG XII, theFundaçãoCoppetec
in Brazil, the CNRS in France, the NetherlandsGraduateSchool for Science, Technology
and Modern Culture WTMC, the State University of New York SUNY, and our
respective departments. We thank Alexander Etzkowitz
for assistance with graphics.

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